Overview

Dataset statistics

Number of variables26
Number of observations1960194
Missing cells163
Missing cells (%)< 0.1%
Duplicate rows101
Duplicate rows (%)< 0.1%
Total size in memory403.8 MiB
Average record size in memory216.0 B

Variable types

Categorical8
DateTime2
Numeric16

Alerts

Dataset has 101 (< 0.1%) duplicate rowsDuplicates
VIN has a high cardinality: 1795923 distinct valuesHigh cardinality
MotorType has a high cardinality: 34099 distinct valuesHigh cardinality
Make has a high cardinality: 833 distinct valuesHigh cardinality
Model has a high cardinality: 14572 distinct valuesHigh cardinality
Type is highly imbalanced (78.2%)Imbalance
Make is highly imbalanced (56.2%)Imbalance
VehicleType is highly imbalanced (62.9%)Imbalance
VehicleClass is highly imbalanced (79.0%)Imbalance
Result is highly imbalanced (64.2%)Imbalance
Defects9 is highly skewed (γ1 = 39.03479387)Skewed
VIN is uniformly distributedUniform
DefectsA has 134716 (6.9%) zerosZeros
DefectsB has 1740229 (88.8%) zerosZeros
DefectsC has 1931415 (98.5%) zerosZeros
Defects0 has 1755269 (89.5%) zerosZeros
Defects1 has 973178 (49.6%) zerosZeros
Defects2 has 1668681 (85.1%) zerosZeros
Defects3 has 1676817 (85.5%) zerosZeros
Defects4 has 1132803 (57.8%) zerosZeros
Defects5 has 914588 (46.7%) zerosZeros
Defects6 has 475667 (24.3%) zerosZeros
Defects7 has 1924033 (98.2%) zerosZeros
Defects8 has 1921974 (98.1%) zerosZeros
Defects9 has 1957425 (99.9%) zerosZeros

Reproduction

Analysis started2023-04-17 14:09:52.919881
Analysis finished2023-04-17 14:11:17.464335
Duration1 minute and 24.54 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

Type
Categorical

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.9 MiB
pravidelná
1688510 
opakovaná
 
140189
před registrací
 
85098
evidenční
 
36466
na žádost zákazníka
 
6005
Other values (8)
 
3926

Length

Max length37
Median length10
Mean length10.186385
Min length3

Characters and Unicode

Total characters19967290
Distinct characters30
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowpravidelná
2nd rowpravidelná
3rd rowpravidelná
4th rowpravidelná
5th rowpravidelná

Common Values

ValueCountFrequency (%)
pravidelná 1688510
86.1%
opakovaná 140189
 
7.2%
před registrací 85098
 
4.3%
evidenční 36466
 
1.9%
na žádost zákazníka 6005
 
0.3%
před registrací - opakovaná 2514
 
0.1%
před schválením tech. zp. 848
 
< 0.1%
silniční - opakovaná po DN 310
 
< 0.1%
silniční - opakovaná 120
 
< 0.1%
ADR 63
 
< 0.1%
Other values (3) 71
 
< 0.1%

Length

2023-04-17T16:11:17.504416image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pravidelná 1688510
81.6%
opakovaná 143184
 
6.9%
před 88509
 
4.3%
registrací 87612
 
4.2%
evidenční 36466
 
1.8%
na 6005
 
0.3%
žádost 6005
 
0.3%
zákazníka 6005
 
0.3%
2995
 
0.1%
schválením 897
 
< 0.1%
Other values (7) 2929
 
0.1%

Most occurring characters

ValueCountFrequency (%)
a 2080525
10.4%
e 1939377
9.7%
p 1921410
9.6%
n 1918433
9.6%
v 1869057
9.4%
r 1863734
9.3%
á 1844621
9.2%
d 1819490
9.1%
i 1813448
9.1%
l 1689837
8.5%
Other values (20) 1207358
6.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 19852763
99.4%
Space Separator 108923
 
0.5%
Dash Punctuation 2995
 
< 0.1%
Other Punctuation 1794
 
< 0.1%
Uppercase Letter 815
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2080525
10.5%
e 1939377
9.8%
p 1921410
9.7%
n 1918433
9.7%
v 1869057
9.4%
r 1863734
9.4%
á 1844621
9.3%
d 1819490
9.2%
i 1813448
9.1%
l 1689837
8.5%
Other values (13) 1092831
5.5%
Uppercase Letter
ValueCountFrequency (%)
D 375
46.0%
N 310
38.0%
A 65
 
8.0%
R 65
 
8.0%
Space Separator
ValueCountFrequency (%)
108923
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2995
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1794
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 19853578
99.4%
Common 113712
 
0.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2080525
10.5%
e 1939377
9.8%
p 1921410
9.7%
n 1918433
9.7%
v 1869057
9.4%
r 1863734
9.4%
á 1844621
9.3%
d 1819490
9.2%
i 1813448
9.1%
l 1689837
8.5%
Other values (17) 1093646
5.5%
Common
ValueCountFrequency (%)
108923
95.8%
- 2995
 
2.6%
. 1794
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17859809
89.4%
None 2107481
 
10.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 2080525
11.6%
e 1939377
10.9%
p 1921410
10.8%
n 1918433
10.7%
v 1869057
10.5%
r 1863734
10.4%
d 1819490
10.2%
i 1813448
10.2%
l 1689837
9.5%
o 292683
 
1.6%
Other values (15) 651815
 
3.6%
None
ValueCountFrequency (%)
á 1844621
87.5%
í 131430
 
6.2%
ř 88529
 
4.2%
č 36896
 
1.8%
ž 6005
 
0.3%

VIN
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct1795923
Distinct (%)91.6%
Missing0
Missing (%)0.0%
Memory size29.9 MiB
WMAR12ZZ99T014298
 
6
YV1LS7202Y2664839
 
6
VF31CK9Y250472917
 
6
NMTEC28E90R022003
 
6
W0L0TGF35X8031870
 
5
Other values (1795918)
1960165 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters33323298
Distinct characters40
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1639550 ?
Unique (%)83.6%

Sample

1st rowW0LF7A0A6CV620692
2nd rowWDB1241251A643342
3rd rowWVWZZZ7MZWV058428
4th rowVF625GPA000000578
5th rowYS2R4X20005353817

Common Values

ValueCountFrequency (%)
WMAR12ZZ99T014298 6
 
< 0.1%
YV1LS7202Y2664839 6
 
< 0.1%
VF31CK9Y250472917 6
 
< 0.1%
NMTEC28E90R022003 6
 
< 0.1%
W0L0TGF35X8031870 5
 
< 0.1%
5GTEN13E788134109 5
 
< 0.1%
WF0LM2E406W517821 5
 
< 0.1%
WF05XXGBB52U40104 5
 
< 0.1%
TMBKG21U6Y8276908 5
 
< 0.1%
WVWZZZ3BZ4E265329 5
 
< 0.1%
Other values (1795913) 1960140
> 99.9%

Length

2023-04-17T16:11:17.623970image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
wmar12zz99t014298 6
 
< 0.1%
vf31ck9y250472917 6
 
< 0.1%
nmtec28e90r022003 6
 
< 0.1%
yv1ls7202y2664839 6
 
< 0.1%
yv2js02cx7a645139 5
 
< 0.1%
tmad381uaej068089 5
 
< 0.1%
wauzzz8p86a130319 5
 
< 0.1%
vsktvur20u0420932 5
 
< 0.1%
wv2zzz7hz5h076723 5
 
< 0.1%
6fppxxmj2pgr50289 5
 
< 0.1%
Other values (1795914) 1960141
> 99.9%

Most occurring characters

ValueCountFrequency (%)
0 3064279
 
9.2%
1 2621313
 
7.9%
2 2169130
 
6.5%
3 1978494
 
5.9%
6 1797428
 
5.4%
4 1769371
 
5.3%
5 1715536
 
5.1%
7 1534703
 
4.6%
Z 1501795
 
4.5%
8 1490707
 
4.5%
Other values (30) 13680542
41.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 19447609
58.4%
Uppercase Letter 13875636
41.6%
Dash Punctuation 43
 
< 0.1%
Other Punctuation 9
 
< 0.1%
Space Separator 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
Z 1501795
 
10.8%
B 1158968
 
8.4%
F 1073545
 
7.7%
W 1021016
 
7.4%
M 897295
 
6.5%
T 829798
 
6.0%
A 745133
 
5.4%
V 734550
 
5.3%
X 599190
 
4.3%
C 542324
 
3.9%
Other values (16) 4772022
34.4%
Decimal Number
ValueCountFrequency (%)
0 3064279
15.8%
1 2621313
13.5%
2 2169130
11.2%
3 1978494
10.2%
6 1797428
9.2%
4 1769371
9.1%
5 1715536
8.8%
7 1534703
7.9%
8 1490707
7.7%
9 1306648
6.7%
Other Punctuation
ValueCountFrequency (%)
. 6
66.7%
/ 3
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 43
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 19447662
58.4%
Latin 13875636
41.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
Z 1501795
 
10.8%
B 1158968
 
8.4%
F 1073545
 
7.7%
W 1021016
 
7.4%
M 897295
 
6.5%
T 829798
 
6.0%
A 745133
 
5.4%
V 734550
 
5.3%
X 599190
 
4.3%
C 542324
 
3.9%
Other values (16) 4772022
34.4%
Common
ValueCountFrequency (%)
0 3064279
15.8%
1 2621313
13.5%
2 2169130
11.2%
3 1978494
10.2%
6 1797428
9.2%
4 1769371
9.1%
5 1715536
8.8%
7 1534703
7.9%
8 1490707
7.7%
9 1306648
6.7%
Other values (4) 53
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33323298
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3064279
 
9.2%
1 2621313
 
7.9%
2 2169130
 
6.5%
3 1978494
 
5.9%
6 1797428
 
5.4%
4 1769371
 
5.3%
5 1715536
 
5.1%
7 1534703
 
4.6%
Z 1501795
 
4.5%
8 1490707
 
4.5%
Other values (30) 13680542
41.1%

Date
Date

Distinct329
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.9 MiB
Minimum2020-01-02 00:00:00
Maximum2020-12-31 00:00:00
2023-04-17T16:11:17.702907image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:11:17.789115image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

MotorType
Categorical

Distinct34099
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size29.9 MiB
ALH
 
28016
BXE
 
25666
AQW
 
20186
ASV
 
19698
781.136M
 
19698
Other values (34094)
1846930 

Length

Max length17
Median length16
Mean length4.5541385
Min length1

Characters and Unicode

Total characters8926995
Distinct characters88
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17005 ?
Unique (%)0.9%

Sample

1st rowM9RA630
2nd row602.912
3rd rowAFN
4th rowDXI11
5th rowDC13 125

Common Values

ValueCountFrequency (%)
ALH 28016
 
1.4%
BXE 25666
 
1.3%
AQW 20186
 
1.0%
ASV 19698
 
1.0%
781.136M 19698
 
1.0%
AGR 17969
 
0.9%
AZQ 17296
 
0.9%
BME 16180
 
0.8%
AWY 15410
 
0.8%
NFU 15401
 
0.8%
Other values (34089) 1764674
90.0%

Length

2023-04-17T16:11:17.877748image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
alh 28052
 
1.3%
bxe 25667
 
1.2%
7 21581
 
1.0%
aqw 20310
 
0.9%
781.136m 19887
 
0.9%
asv 19719
 
0.9%
781.136 18986
 
0.9%
agr 18021
 
0.8%
m 17558
 
0.8%
azq 17300
 
0.8%
Other values (25613) 2000082
90.6%

Most occurring characters

ValueCountFrequency (%)
A 791147
 
8.9%
1 553028
 
6.2%
B 525490
 
5.9%
F 465135
 
5.2%
4 392837
 
4.4%
D 380661
 
4.3%
0 372252
 
4.2%
6 291556
 
3.3%
C 284854
 
3.2%
E 275643
 
3.1%
Other values (78) 4594392
51.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 5497379
61.6%
Decimal Number 2955307
33.1%
Space Separator 247824
 
2.8%
Other Punctuation 172758
 
1.9%
Dash Punctuation 50396
 
0.6%
Math Symbol 1480
 
< 0.1%
Open Punctuation 627
 
< 0.1%
Close Punctuation 616
 
< 0.1%
Lowercase Letter 607
 
< 0.1%
Modifier Symbol 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 791147
14.4%
B 525490
 
9.6%
F 465135
 
8.5%
D 380661
 
6.9%
C 284854
 
5.2%
E 275643
 
5.0%
H 260544
 
4.7%
M 251948
 
4.6%
K 214843
 
3.9%
X 207801
 
3.8%
Other values (25) 1839313
33.5%
Lowercase Letter
ValueCountFrequency (%)
a 79
 
13.0%
b 48
 
7.9%
c 45
 
7.4%
f 40
 
6.6%
s 33
 
5.4%
d 31
 
5.1%
h 29
 
4.8%
x 28
 
4.6%
l 27
 
4.4%
e 23
 
3.8%
Other values (20) 224
36.9%
Decimal Number
ValueCountFrequency (%)
1 553028
18.7%
4 392837
13.3%
0 372252
12.6%
6 291556
9.9%
2 265491
9.0%
7 253789
8.6%
8 251604
8.5%
3 242868
8.2%
9 202504
 
6.9%
5 129378
 
4.4%
Other Punctuation
ValueCountFrequency (%)
. 153503
88.9%
/ 14130
 
8.2%
, 2988
 
1.7%
* 2111
 
1.2%
? 22
 
< 0.1%
; 4
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 615
99.8%
] 1
 
0.2%
Space Separator
ValueCountFrequency (%)
247824
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 50396
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1480
100.0%
Open Punctuation
ValueCountFrequency (%)
( 627
100.0%
Modifier Symbol
ValueCountFrequency (%)
˙ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5497986
61.6%
Common 3429009
38.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 791147
14.4%
B 525490
 
9.6%
F 465135
 
8.5%
D 380661
 
6.9%
C 284854
 
5.2%
E 275643
 
5.0%
H 260544
 
4.7%
M 251948
 
4.6%
K 214843
 
3.9%
X 207801
 
3.8%
Other values (55) 1839920
33.5%
Common
ValueCountFrequency (%)
1 553028
16.1%
4 392837
11.5%
0 372252
10.9%
6 291556
8.5%
2 265491
7.7%
7 253789
7.4%
8 251604
7.3%
247824
7.2%
3 242868
7.1%
9 202504
 
5.9%
Other values (13) 355256
10.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8925807
> 99.9%
None 1187
 
< 0.1%
Modifier Letters 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 791147
 
8.9%
1 553028
 
6.2%
B 525490
 
5.9%
F 465135
 
5.2%
4 392837
 
4.4%
D 380661
 
4.3%
0 372252
 
4.2%
6 291556
 
3.3%
C 284854
 
3.2%
E 275643
 
3.1%
Other values (64) 4593204
51.5%
None
ValueCountFrequency (%)
Š 1072
90.3%
Á 27
 
2.3%
Č 26
 
2.2%
Ý 17
 
1.4%
š 8
 
0.7%
Ě 8
 
0.7%
Ž 8
 
0.7%
Í 7
 
0.6%
Ř 6
 
0.5%
ý 3
 
0.3%
Other values (3) 5
 
0.4%
Modifier Letters
ValueCountFrequency (%)
˙ 1
100.0%

Make
Categorical

HIGH CARDINALITY  IMBALANCE 

Distinct833
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.9 MiB
ŠKODA
554965 
FORD
167898 
VOLKSWAGEN
132231 
RENAULT
118106 
PEUGEOT
116077 
Other values (828)
870917 

Length

Max length30
Median length28
Mean length5.6987349
Min length2

Characters and Unicode

Total characters11170626
Distinct characters77
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique278 ?
Unique (%)< 0.1%

Sample

1st rowOPEL
2nd rowMERCEDES
3rd rowVOLKSWAGEN
4th rowRENAULT
5th rowSCANIA

Common Values

ValueCountFrequency (%)
ŠKODA 554965
28.3%
FORD 167898
 
8.6%
VOLKSWAGEN 132231
 
6.7%
RENAULT 118106
 
6.0%
PEUGEOT 116077
 
5.9%
CITROËN 85420
 
4.4%
VW 82432
 
4.2%
OPEL 68763
 
3.5%
FIAT 57518
 
2.9%
MERCEDES-BENZ 56651
 
2.9%
Other values (823) 520133
26.5%

Length

2023-04-17T16:11:17.965034image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
škoda 554981
28.1%
ford 167906
 
8.5%
volkswagen 132231
 
6.7%
renault 118140
 
6.0%
peugeot 116078
 
5.9%
citroën 85420
 
4.3%
vw 82432
 
4.2%
opel 68763
 
3.5%
fiat 57527
 
2.9%
mercedes-benz 56652
 
2.9%
Other values (836) 534777
27.1%

Most occurring characters

ValueCountFrequency (%)
O 1354218
12.1%
A 1302264
 
11.7%
D 948667
 
8.5%
E 900691
 
8.1%
K 740351
 
6.6%
Š 554980
 
5.0%
N 547009
 
4.9%
T 531948
 
4.8%
R 500724
 
4.5%
I 416225
 
3.7%
Other values (67) 3373549
30.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 11098015
99.3%
Dash Punctuation 57237
 
0.5%
Space Separator 14720
 
0.1%
Lowercase Letter 361
 
< 0.1%
Other Punctuation 201
 
< 0.1%
Decimal Number 88
 
< 0.1%
Math Symbol 2
 
< 0.1%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 1354218
12.2%
A 1302264
 
11.7%
D 948667
 
8.5%
E 900691
 
8.1%
K 740351
 
6.7%
Š 554980
 
5.0%
N 547009
 
4.9%
T 531948
 
4.8%
R 500724
 
4.5%
I 416225
 
3.8%
Other values (27) 3300938
29.7%
Lowercase Letter
ValueCountFrequency (%)
a 87
24.1%
l 49
13.6%
o 29
 
8.0%
h 27
 
7.5%
u 26
 
7.2%
x 24
 
6.6%
d 21
 
5.8%
k 19
 
5.3%
š 14
 
3.9%
e 14
 
3.9%
Other values (13) 51
14.1%
Decimal Number
ValueCountFrequency (%)
0 25
28.4%
1 23
26.1%
2 16
18.2%
5 8
 
9.1%
3 6
 
6.8%
4 5
 
5.7%
9 3
 
3.4%
6 2
 
2.3%
Other Punctuation
ValueCountFrequency (%)
. 165
82.1%
/ 33
 
16.4%
, 2
 
1.0%
& 1
 
0.5%
Dash Punctuation
ValueCountFrequency (%)
- 57237
100.0%
Space Separator
ValueCountFrequency (%)
14720
100.0%
Math Symbol
ValueCountFrequency (%)
+ 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11098376
99.4%
Common 72250
 
0.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 1354218
12.2%
A 1302264
 
11.7%
D 948667
 
8.5%
E 900691
 
8.1%
K 740351
 
6.7%
Š 554980
 
5.0%
N 547009
 
4.9%
T 531948
 
4.8%
R 500724
 
4.5%
I 416225
 
3.8%
Other values (50) 3301299
29.7%
Common
ValueCountFrequency (%)
- 57237
79.2%
14720
 
20.4%
. 165
 
0.2%
/ 33
 
< 0.1%
0 25
 
< 0.1%
1 23
 
< 0.1%
2 16
 
< 0.1%
5 8
 
< 0.1%
3 6
 
< 0.1%
4 5
 
< 0.1%
Other values (7) 12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10530072
94.3%
None 640554
 
5.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 1354218
12.9%
A 1302264
12.4%
D 948667
 
9.0%
E 900691
 
8.6%
K 740351
 
7.0%
N 547009
 
5.2%
T 531948
 
5.1%
R 500724
 
4.8%
I 416225
 
4.0%
U 399120
 
3.8%
Other values (54) 2888855
27.4%
None
ValueCountFrequency (%)
Š 554980
86.6%
Ë 85421
 
13.3%
Č 46
 
< 0.1%
Ü 26
 
< 0.1%
Á 24
 
< 0.1%
Ö 21
 
< 0.1%
š 14
 
< 0.1%
Ž 8
 
< 0.1%
Ě 8
 
< 0.1%
Í 3
 
< 0.1%
Other values (3) 3
 
< 0.1%

VehicleType
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.9 MiB
OSOBNÍ AUTOMOBIL
1621852 
NÁKLADNÍ AUTOMOBIL
312935 
AUTOBUS
 
13531
MOTOCYKL
 
11876

Length

Max length18
Median length16
Mean length16.208695
Min length7

Characters and Unicode

Total characters31772187
Distinct characters17
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOSOBNÍ AUTOMOBIL
2nd rowOSOBNÍ AUTOMOBIL
3rd rowOSOBNÍ AUTOMOBIL
4th rowNÁKLADNÍ AUTOMOBIL
5th rowNÁKLADNÍ AUTOMOBIL

Common Values

ValueCountFrequency (%)
OSOBNÍ AUTOMOBIL 1621852
82.7%
NÁKLADNÍ AUTOMOBIL 312935
 
16.0%
AUTOBUS 13531
 
0.7%
MOTOCYKL 11876
 
0.6%

Length

2023-04-17T16:11:18.042844image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-17T16:11:18.132457image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
automobil 1934787
49.7%
osobní 1621852
41.6%
nákladní 312935
 
8.0%
autobus 13531
 
0.3%
motocykl 11876
 
0.3%

Most occurring characters

ValueCountFrequency (%)
O 7150561
22.5%
B 3570170
11.2%
A 2261253
 
7.1%
L 2259598
 
7.1%
N 2247722
 
7.1%
U 1961849
 
6.2%
T 1960194
 
6.2%
M 1946663
 
6.1%
1934787
 
6.1%
I 1934787
 
6.1%
Other values (7) 4544603
14.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 29837400
93.9%
Space Separator 1934787
 
6.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 7150561
24.0%
B 3570170
12.0%
A 2261253
 
7.6%
L 2259598
 
7.6%
N 2247722
 
7.5%
U 1961849
 
6.6%
T 1960194
 
6.6%
M 1946663
 
6.5%
I 1934787
 
6.5%
Í 1934787
 
6.5%
Other values (6) 2609816
 
8.7%
Space Separator
ValueCountFrequency (%)
1934787
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 29837400
93.9%
Common 1934787
 
6.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 7150561
24.0%
B 3570170
12.0%
A 2261253
 
7.6%
L 2259598
 
7.6%
N 2247722
 
7.5%
U 1961849
 
6.6%
T 1960194
 
6.6%
M 1946663
 
6.5%
I 1934787
 
6.5%
Í 1934787
 
6.5%
Other values (6) 2609816
 
8.7%
Common
ValueCountFrequency (%)
1934787
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29524465
92.9%
None 2247722
 
7.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 7150561
24.2%
B 3570170
12.1%
A 2261253
 
7.7%
L 2259598
 
7.7%
N 2247722
 
7.6%
U 1961849
 
6.6%
T 1960194
 
6.6%
M 1946663
 
6.6%
1934787
 
6.6%
I 1934787
 
6.6%
Other values (5) 2296881
 
7.8%
None
ValueCountFrequency (%)
Í 1934787
86.1%
Á 312935
 
13.9%

Model
Categorical

Distinct14572
Distinct (%)0.7%
Missing163
Missing (%)< 0.1%
Memory size29.9 MiB
OCTAVIA
191721 
FABIA
163423 
FELICIA
 
50005
FOCUS
 
46525
GOLF
 
44461
Other values (14567)
1463896 

Length

Max length30
Median length28
Mean length6.1983499
Min length1

Characters and Unicode

Total characters12148958
Distinct characters77
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7035 ?
Unique (%)0.4%

Sample

1st rowVIVARO
2nd row124
3rd rowSHARAN
4th rowPREMIUM
5th rowR 480

Common Values

ValueCountFrequency (%)
OCTAVIA 191721
 
9.8%
FABIA 163423
 
8.3%
FELICIA 50005
 
2.6%
FOCUS 46525
 
2.4%
GOLF 44461
 
2.3%
FABIA COMBI 40692
 
2.1%
OCTAVIA COMBI 30578
 
1.6%
TRANSIT 27770
 
1.4%
206 27411
 
1.4%
PASSAT 26612
 
1.4%
Other values (14562) 1310833
66.9%

Length

2023-04-17T16:11:18.212875image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
octavia 222308
 
9.6%
fabia 206295
 
8.9%
combi 82449
 
3.6%
felicia 60100
 
2.6%
golf 54900
 
2.4%
focus 51870
 
2.2%
passat 46690
 
2.0%
megane 41489
 
1.8%
transit 30108
 
1.3%
206 27832
 
1.2%
Other values (9400) 1483472
64.3%

Most occurring characters

ValueCountFrequency (%)
A 1931327
15.9%
I 1045347
 
8.6%
O 937602
 
7.7%
C 767576
 
6.3%
T 764827
 
6.3%
R 640400
 
5.3%
E 592769
 
4.9%
S 556763
 
4.6%
F 482050
 
4.0%
N 437700
 
3.6%
Other values (67) 3992597
32.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 10776166
88.7%
Decimal Number 1003101
 
8.3%
Space Separator 347488
 
2.9%
Lowercase Letter 22203
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 1931327
17.9%
I 1045347
 
9.7%
O 937602
 
8.7%
C 767576
 
7.1%
T 764827
 
7.1%
R 640400
 
5.9%
E 592769
 
5.5%
S 556763
 
5.2%
F 482050
 
4.5%
N 437700
 
4.1%
Other values (28) 2619805
24.3%
Lowercase Letter
ValueCountFrequency (%)
i 13335
60.1%
o 2005
 
9.0%
r 1803
 
8.1%
a 1752
 
7.9%
e 555
 
2.5%
x 472
 
2.1%
n 412
 
1.9%
t 401
 
1.8%
d 277
 
1.2%
s 259
 
1.2%
Other values (18) 932
 
4.2%
Decimal Number
ValueCountFrequency (%)
0 253202
25.2%
3 131225
13.1%
2 126445
12.6%
1 102723
10.2%
6 91270
 
9.1%
5 88858
 
8.9%
4 82764
 
8.3%
7 62112
 
6.2%
8 46319
 
4.6%
9 18183
 
1.8%
Space Separator
ValueCountFrequency (%)
347488
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10798369
88.9%
Common 1350589
 
11.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 1931327
17.9%
I 1045347
 
9.7%
O 937602
 
8.7%
C 767576
 
7.1%
T 764827
 
7.1%
R 640400
 
5.9%
E 592769
 
5.5%
S 556763
 
5.2%
F 482050
 
4.5%
N 437700
 
4.1%
Other values (56) 2642008
24.5%
Common
ValueCountFrequency (%)
347488
25.7%
0 253202
18.7%
3 131225
 
9.7%
2 126445
 
9.4%
1 102723
 
7.6%
6 91270
 
6.8%
5 88858
 
6.6%
4 82764
 
6.1%
7 62112
 
4.6%
8 46319
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12148002
> 99.9%
None 956
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 1931327
15.9%
I 1045347
 
8.6%
O 937602
 
7.7%
C 767576
 
6.3%
T 764827
 
6.3%
R 640400
 
5.3%
E 592769
 
4.9%
S 556763
 
4.6%
F 482050
 
4.0%
N 437700
 
3.6%
Other values (53) 3991641
32.9%
None
ValueCountFrequency (%)
Á 588
61.5%
É 303
31.7%
á 20
 
2.1%
Ó 9
 
0.9%
Š 8
 
0.8%
Ý 7
 
0.7%
Č 6
 
0.6%
Ö 5
 
0.5%
Ě 3
 
0.3%
Ž 2
 
0.2%
Other values (4) 5
 
0.5%

VehicleClass
Categorical

Distinct45
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.9 MiB
M1
1584025 
N1
195796 
N3
 
52533
M1G
 
37827
N2
 
37351
Other values (40)
 
52662

Length

Max length7
Median length2
Mean length2.0359623
Min length1

Characters and Unicode

Total characters3990881
Distinct characters23
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)< 0.1%

Sample

1st rowM1
2nd rowM1
3rd rowM1
4th rowN3
5th rowN3

Common Values

ValueCountFrequency (%)
M1 1584025
80.8%
N1 195796
 
10.0%
N3 52533
 
2.7%
M1G 37827
 
1.9%
N2 37351
 
1.9%
N1G 15872
 
0.8%
M3 12449
 
0.6%
N3G 10759
 
0.5%
LC 5047
 
0.3%
L3e 3437
 
0.2%
Other values (35) 5098
 
0.3%

Length

2023-04-17T16:11:18.294036image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
m1 1584025
80.8%
n1 195796
 
10.0%
n3 52533
 
2.7%
m1g 37827
 
1.9%
n2 37351
 
1.9%
n1g 15872
 
0.8%
m3 12449
 
0.6%
n3g 10759
 
0.5%
lc 5047
 
0.3%
l3e 3437
 
0.2%
Other values (35) 5098
 
0.3%

Most occurring characters

ValueCountFrequency (%)
1 1834138
46.0%
M 1635383
41.0%
N 312935
 
7.8%
3 79494
 
2.0%
G 65078
 
1.6%
2 39113
 
1.0%
L 11875
 
0.3%
C 5049
 
0.1%
e 4691
 
0.1%
A 1632
 
< 0.1%
Other values (13) 1493
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2032698
50.9%
Decimal Number 1953253
48.9%
Lowercase Letter 4691
 
0.1%
Dash Punctuation 239
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 1635383
80.5%
N 312935
 
15.4%
G 65078
 
3.2%
L 11875
 
0.6%
C 5049
 
0.2%
A 1632
 
0.1%
E 587
 
< 0.1%
B 145
 
< 0.1%
S 5
 
< 0.1%
D 3
 
< 0.1%
Other values (4) 6
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
1 1834138
93.9%
3 79494
 
4.1%
2 39113
 
2.0%
7 351
 
< 0.1%
6 98
 
< 0.1%
5 54
 
< 0.1%
4 5
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
e 4691
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 239
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2037389
51.1%
Common 1953492
48.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 1635383
80.3%
N 312935
 
15.4%
G 65078
 
3.2%
L 11875
 
0.6%
C 5049
 
0.2%
e 4691
 
0.2%
A 1632
 
0.1%
E 587
 
< 0.1%
B 145
 
< 0.1%
S 5
 
< 0.1%
Other values (5) 9
 
< 0.1%
Common
ValueCountFrequency (%)
1 1834138
93.9%
3 79494
 
4.1%
2 39113
 
2.0%
7 351
 
< 0.1%
- 239
 
< 0.1%
6 98
 
< 0.1%
5 54
 
< 0.1%
4 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3990881
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1834138
46.0%
M 1635383
41.0%
N 312935
 
7.8%
3 79494
 
2.0%
G 65078
 
1.6%
2 39113
 
1.0%
L 11875
 
0.3%
C 5049
 
0.1%
e 4691
 
0.1%
A 1632
 
< 0.1%
Other values (13) 1493
 
< 0.1%
Distinct12223
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size29.9 MiB
Minimum1900-06-05 00:00:00
Maximum2020-12-23 00:00:00
2023-04-17T16:11:18.580618image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:11:18.668798image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Km
Real number (ℝ)

Distinct456417
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean211593.66
Minimum0
Maximum999999
Zeros2387
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size29.9 MiB
2023-04-17T16:11:18.763810image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile59452
Q1134667.25
median195512
Q3263126.75
95-th percentile409331.35
Maximum999999
Range999999
Interquartile range (IQR)128459.5

Descriptive statistics

Standard deviation120973.98
Coefficient of variation (CV)0.5717278
Kurtosis6.8826175
Mean211593.66
Median Absolute Deviation (MAD)63895
Skewness1.8785594
Sum4.1476462 × 1011
Variance1.4634703 × 1010
MonotonicityNot monotonic
2023-04-17T16:11:18.856429image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2387
 
0.1%
399999 125
 
< 0.1%
12 53
 
< 0.1%
999999 53
 
< 0.1%
7 49
 
< 0.1%
5 47
 
< 0.1%
11 47
 
< 0.1%
15 46
 
< 0.1%
6 46
 
< 0.1%
9 44
 
< 0.1%
Other values (456407) 1957297
99.9%
ValueCountFrequency (%)
0 2387
0.1%
1 27
 
< 0.1%
2 24
 
< 0.1%
3 41
 
< 0.1%
4 34
 
< 0.1%
5 47
 
< 0.1%
6 46
 
< 0.1%
7 49
 
< 0.1%
8 35
 
< 0.1%
9 44
 
< 0.1%
ValueCountFrequency (%)
999999 53
< 0.1%
999957 1
 
< 0.1%
999949 1
 
< 0.1%
999936 1
 
< 0.1%
999898 1
 
< 0.1%
999854 1
 
< 0.1%
999833 1
 
< 0.1%
999786 1
 
< 0.1%
999753 1
 
< 0.1%
999682 1
 
< 0.1%

Result
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size29.9 MiB
způsobilé
1734177 
částečně způsobilé
204802 
nezpůsobilé
 
21215

Length

Max length18
Median length9
Mean length9.9619701
Min length9

Characters and Unicode

Total characters19527394
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowzpůsobilé
2nd rowzpůsobilé
3rd rowzpůsobilé
4th rowzpůsobilé
5th rowzpůsobilé

Common Values

ValueCountFrequency (%)
způsobilé 1734177
88.5%
částečně způsobilé 204802
 
10.4%
nezpůsobilé 21215
 
1.1%

Length

2023-04-17T16:11:18.934320image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-17T16:11:19.018433image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
způsobilé 1938979
89.6%
částečně 204802
 
9.5%
nezpůsobilé 21215
 
1.0%

Most occurring characters

ValueCountFrequency (%)
s 2164996
11.1%
z 1960194
10.0%
p 1960194
10.0%
ů 1960194
10.0%
o 1960194
10.0%
b 1960194
10.0%
i 1960194
10.0%
l 1960194
10.0%
é 1960194
10.0%
č 409604
 
2.1%
Other values (6) 1271242
6.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 19322592
99.0%
Space Separator 204802
 
1.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 2164996
11.2%
z 1960194
10.1%
p 1960194
10.1%
ů 1960194
10.1%
o 1960194
10.1%
b 1960194
10.1%
i 1960194
10.1%
l 1960194
10.1%
é 1960194
10.1%
č 409604
 
2.1%
Other values (5) 1066440
5.5%
Space Separator
ValueCountFrequency (%)
204802
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 19322592
99.0%
Common 204802
 
1.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 2164996
11.2%
z 1960194
10.1%
p 1960194
10.1%
ů 1960194
10.1%
o 1960194
10.1%
b 1960194
10.1%
i 1960194
10.1%
l 1960194
10.1%
é 1960194
10.1%
č 409604
 
2.1%
Other values (5) 1066440
5.5%
Common
ValueCountFrequency (%)
204802
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14787798
75.7%
None 4739596
 
24.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 2164996
14.6%
z 1960194
13.3%
p 1960194
13.3%
o 1960194
13.3%
b 1960194
13.3%
i 1960194
13.3%
l 1960194
13.3%
e 226017
 
1.5%
n 226017
 
1.5%
t 204802
 
1.4%
None
ValueCountFrequency (%)
ů 1960194
41.4%
é 1960194
41.4%
č 409604
 
8.6%
á 204802
 
4.3%
ě 204802
 
4.3%

Weekday
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9278398
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.9 MiB
2023-04-17T16:11:19.074798image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3750362
Coefficient of variation (CV)0.46964189
Kurtosis-1.1095387
Mean2.9278398
Median Absolute Deviation (MAD)1
Skewness0.098447137
Sum5739134
Variance1.8907246
MonotonicityNot monotonic
2023-04-17T16:11:19.126700image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 431133
22.0%
4 418229
21.3%
2 412809
21.1%
1 390384
19.9%
5 289093
14.7%
6 18470
 
0.9%
7 76
 
< 0.1%
ValueCountFrequency (%)
1 390384
19.9%
2 412809
21.1%
3 431133
22.0%
4 418229
21.3%
5 289093
14.7%
6 18470
 
0.9%
7 76
 
< 0.1%
ValueCountFrequency (%)
7 76
 
< 0.1%
6 18470
 
0.9%
5 289093
14.7%
4 418229
21.3%
3 431133
22.0%
2 412809
21.1%
1 390384
19.9%

DefectsA
Real number (ℝ)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1205401
Minimum0
Maximum32
Zeros134716
Zeros (%)6.9%
Negative0
Negative (%)0.0%
Memory size29.9 MiB
2023-04-17T16:11:19.198335image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34
95-th percentile7
Maximum32
Range32
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.2732218
Coefficient of variation (CV)0.72847062
Kurtosis2.0902012
Mean3.1205401
Median Absolute Deviation (MAD)2
Skewness1.1309683
Sum6116864
Variance5.1675374
MonotonicityNot monotonic
2023-04-17T16:11:19.270005image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 411216
21.0%
2 366519
18.7%
3 319807
16.3%
4 263320
13.4%
5 190608
9.7%
0 134716
 
6.9%
6 113613
 
5.8%
7 68387
 
3.5%
8 40263
 
2.1%
9 24842
 
1.3%
Other values (21) 26903
 
1.4%
ValueCountFrequency (%)
0 134716
 
6.9%
1 411216
21.0%
2 366519
18.7%
3 319807
16.3%
4 263320
13.4%
5 190608
9.7%
6 113613
 
5.8%
7 68387
 
3.5%
8 40263
 
2.1%
9 24842
 
1.3%
ValueCountFrequency (%)
32 1
 
< 0.1%
31 1
 
< 0.1%
29 1
 
< 0.1%
27 3
 
< 0.1%
26 4
 
< 0.1%
25 7
 
< 0.1%
24 6
 
< 0.1%
23 16
< 0.1%
22 22
< 0.1%
21 39
< 0.1%

DefectsB
Real number (ℝ)

Distinct30
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.27757865
Minimum0
Maximum33
Zeros1740229
Zeros (%)88.8%
Negative0
Negative (%)0.0%
Memory size29.9 MiB
2023-04-17T16:11:19.347420image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum33
Range33
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.0217901
Coefficient of variation (CV)3.6810831
Kurtosis43.497147
Mean0.27757865
Median Absolute Deviation (MAD)0
Skewness5.5625767
Sum544108
Variance1.044055
MonotonicityNot monotonic
2023-04-17T16:11:19.420673image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 1740229
88.8%
1 93248
 
4.8%
2 48169
 
2.5%
3 31161
 
1.6%
4 19156
 
1.0%
5 11438
 
0.6%
6 6657
 
0.3%
7 3940
 
0.2%
8 2394
 
0.1%
9 1504
 
0.1%
Other values (20) 2298
 
0.1%
ValueCountFrequency (%)
0 1740229
88.8%
1 93248
 
4.8%
2 48169
 
2.5%
3 31161
 
1.6%
4 19156
 
1.0%
5 11438
 
0.6%
6 6657
 
0.3%
7 3940
 
0.2%
8 2394
 
0.1%
9 1504
 
0.1%
ValueCountFrequency (%)
33 1
 
< 0.1%
28 2
 
< 0.1%
27 2
 
< 0.1%
26 2
 
< 0.1%
25 3
 
< 0.1%
24 5
< 0.1%
23 2
 
< 0.1%
22 2
 
< 0.1%
21 8
< 0.1%
20 12
< 0.1%

DefectsC
Real number (ℝ)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.018244623
Minimum0
Maximum16
Zeros1931415
Zeros (%)98.5%
Negative0
Negative (%)0.0%
Memory size29.9 MiB
2023-04-17T16:11:19.490943image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum16
Range16
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1682519
Coefficient of variation (CV)9.2219993
Kurtosis323.48497
Mean0.018244623
Median Absolute Deviation (MAD)0
Skewness13.734226
Sum35763
Variance0.028308701
MonotonicityNot monotonic
2023-04-17T16:11:19.553666image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 1931415
98.5%
1 23795
 
1.2%
2 3633
 
0.2%
3 955
 
< 0.1%
4 268
 
< 0.1%
5 74
 
< 0.1%
6 24
 
< 0.1%
7 15
 
< 0.1%
8 5
 
< 0.1%
10 4
 
< 0.1%
Other values (4) 6
 
< 0.1%
ValueCountFrequency (%)
0 1931415
98.5%
1 23795
 
1.2%
2 3633
 
0.2%
3 955
 
< 0.1%
4 268
 
< 0.1%
5 74
 
< 0.1%
6 24
 
< 0.1%
7 15
 
< 0.1%
8 5
 
< 0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
16 1
 
< 0.1%
12 1
 
< 0.1%
11 1
 
< 0.1%
10 4
 
< 0.1%
9 3
 
< 0.1%
8 5
 
< 0.1%
7 15
 
< 0.1%
6 24
 
< 0.1%
5 74
 
< 0.1%
4 268
< 0.1%

Defects0
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.11194045
Minimum0
Maximum6
Zeros1755269
Zeros (%)89.5%
Negative0
Negative (%)0.0%
Memory size29.9 MiB
2023-04-17T16:11:19.620497image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.33877203
Coefficient of variation (CV)3.0263593
Kurtosis9.8253518
Mean0.11194045
Median Absolute Deviation (MAD)0
Skewness3.084591
Sum219425
Variance0.11476649
MonotonicityNot monotonic
2023-04-17T16:11:19.677294image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 1755269
89.5%
1 190927
 
9.7%
2 13538
 
0.7%
3 424
 
< 0.1%
4 31
 
< 0.1%
5 4
 
< 0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
0 1755269
89.5%
1 190927
 
9.7%
2 13538
 
0.7%
3 424
 
< 0.1%
4 31
 
< 0.1%
5 4
 
< 0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
6 1
 
< 0.1%
5 4
 
< 0.1%
4 31
 
< 0.1%
3 424
 
< 0.1%
2 13538
 
0.7%
1 190927
 
9.7%
0 1755269
89.5%

Defects1
Real number (ℝ)

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7536402
Minimum0
Maximum12
Zeros973178
Zeros (%)49.6%
Negative0
Negative (%)0.0%
Memory size29.9 MiB
2023-04-17T16:11:19.739767image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile3
Maximum12
Range12
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.92972616
Coefficient of variation (CV)1.2336472
Kurtosis2.7804578
Mean0.7536402
Median Absolute Deviation (MAD)1
Skewness1.4164686
Sum1477281
Variance0.86439073
MonotonicityNot monotonic
2023-04-17T16:11:19.806500image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 973178
49.6%
1 626900
32.0%
2 261955
 
13.4%
3 74866
 
3.8%
4 17325
 
0.9%
5 4170
 
0.2%
6 1192
 
0.1%
7 401
 
< 0.1%
8 136
 
< 0.1%
9 45
 
< 0.1%
Other values (3) 26
 
< 0.1%
ValueCountFrequency (%)
0 973178
49.6%
1 626900
32.0%
2 261955
 
13.4%
3 74866
 
3.8%
4 17325
 
0.9%
5 4170
 
0.2%
6 1192
 
0.1%
7 401
 
< 0.1%
8 136
 
< 0.1%
9 45
 
< 0.1%
ValueCountFrequency (%)
12 3
 
< 0.1%
11 5
 
< 0.1%
10 18
 
< 0.1%
9 45
 
< 0.1%
8 136
 
< 0.1%
7 401
 
< 0.1%
6 1192
 
0.1%
5 4170
 
0.2%
4 17325
 
0.9%
3 74866
3.8%

Defects2
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.16187377
Minimum0
Maximum6
Zeros1668681
Zeros (%)85.1%
Negative0
Negative (%)0.0%
Memory size29.9 MiB
2023-04-17T16:11:19.869207image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.40495726
Coefficient of variation (CV)2.5016854
Kurtosis6.8725928
Mean0.16187377
Median Absolute Deviation (MAD)0
Skewness2.5523837
Sum317304
Variance0.16399038
MonotonicityNot monotonic
2023-04-17T16:11:19.926392image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 1668681
85.1%
1 267528
 
13.6%
2 22324
 
1.1%
3 1529
 
0.1%
4 120
 
< 0.1%
5 11
 
< 0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
0 1668681
85.1%
1 267528
 
13.6%
2 22324
 
1.1%
3 1529
 
0.1%
4 120
 
< 0.1%
5 11
 
< 0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
6 1
 
< 0.1%
5 11
 
< 0.1%
4 120
 
< 0.1%
3 1529
 
0.1%
2 22324
 
1.1%
1 267528
 
13.6%
0 1668681
85.1%

Defects3
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.16686512
Minimum0
Maximum7
Zeros1676817
Zeros (%)85.5%
Negative0
Negative (%)0.0%
Memory size29.9 MiB
2023-04-17T16:11:19.987897image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.43708423
Coefficient of variation (CV)2.6193865
Kurtosis10.870931
Mean0.16686512
Median Absolute Deviation (MAD)0
Skewness2.9781246
Sum327088
Variance0.19104263
MonotonicityNot monotonic
2023-04-17T16:11:20.047027image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 1676817
85.5%
1 245896
 
12.5%
2 32142
 
1.6%
3 4577
 
0.2%
4 653
 
< 0.1%
5 94
 
< 0.1%
6 10
 
< 0.1%
7 5
 
< 0.1%
ValueCountFrequency (%)
0 1676817
85.5%
1 245896
 
12.5%
2 32142
 
1.6%
3 4577
 
0.2%
4 653
 
< 0.1%
5 94
 
< 0.1%
6 10
 
< 0.1%
7 5
 
< 0.1%
ValueCountFrequency (%)
7 5
 
< 0.1%
6 10
 
< 0.1%
5 94
 
< 0.1%
4 653
 
< 0.1%
3 4577
 
0.2%
2 32142
 
1.6%
1 245896
 
12.5%
0 1676817
85.5%

Defects4
Real number (ℝ)

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.58542675
Minimum0
Maximum18
Zeros1132803
Zeros (%)57.8%
Negative0
Negative (%)0.0%
Memory size29.9 MiB
2023-04-17T16:11:20.114251image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum18
Range18
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.83562538
Coefficient of variation (CV)1.4273782
Kurtosis6.3491103
Mean0.58542675
Median Absolute Deviation (MAD)0
Skewness1.9156935
Sum1147550
Variance0.69826978
MonotonicityNot monotonic
2023-04-17T16:11:20.182448image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 1132803
57.8%
1 592316
30.2%
2 176104
 
9.0%
3 42022
 
2.1%
4 11252
 
0.6%
5 3610
 
0.2%
6 1279
 
0.1%
7 494
 
< 0.1%
8 190
 
< 0.1%
9 61
 
< 0.1%
Other values (8) 63
 
< 0.1%
ValueCountFrequency (%)
0 1132803
57.8%
1 592316
30.2%
2 176104
 
9.0%
3 42022
 
2.1%
4 11252
 
0.6%
5 3610
 
0.2%
6 1279
 
0.1%
7 494
 
< 0.1%
8 190
 
< 0.1%
9 61
 
< 0.1%
ValueCountFrequency (%)
18 1
 
< 0.1%
17 1
 
< 0.1%
15 2
 
< 0.1%
14 2
 
< 0.1%
13 3
 
< 0.1%
12 8
 
< 0.1%
11 13
 
< 0.1%
10 33
 
< 0.1%
9 61
 
< 0.1%
8 190
< 0.1%

Defects5
Real number (ℝ)

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6838869
Minimum0
Maximum10
Zeros914588
Zeros (%)46.7%
Negative0
Negative (%)0.0%
Memory size29.9 MiB
2023-04-17T16:11:20.252792image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile2
Maximum10
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.75938362
Coefficient of variation (CV)1.1103936
Kurtosis1.7993339
Mean0.6838869
Median Absolute Deviation (MAD)1
Skewness1.1023007
Sum1340551
Variance0.57666349
MonotonicityNot monotonic
2023-04-17T16:11:20.314032image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 914588
46.7%
1 798724
40.7%
2 206712
 
10.5%
3 34027
 
1.7%
4 4845
 
0.2%
5 982
 
0.1%
6 227
 
< 0.1%
7 56
 
< 0.1%
8 22
 
< 0.1%
9 8
 
< 0.1%
ValueCountFrequency (%)
0 914588
46.7%
1 798724
40.7%
2 206712
 
10.5%
3 34027
 
1.7%
4 4845
 
0.2%
5 982
 
0.1%
6 227
 
< 0.1%
7 56
 
< 0.1%
8 22
 
< 0.1%
9 8
 
< 0.1%
ValueCountFrequency (%)
10 3
 
< 0.1%
9 8
 
< 0.1%
8 22
 
< 0.1%
7 56
 
< 0.1%
6 227
 
< 0.1%
5 982
 
0.1%
4 4845
 
0.2%
3 34027
 
1.7%
2 206712
 
10.5%
1 798724
40.7%

Defects6
Real number (ℝ)

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.344497
Minimum0
Maximum20
Zeros475667
Zeros (%)24.3%
Negative0
Negative (%)0.0%
Memory size29.9 MiB
2023-04-17T16:11:20.380495image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile3
Maximum20
Range20
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1678664
Coefficient of variation (CV)0.868627
Kurtosis3.0323792
Mean1.344497
Median Absolute Deviation (MAD)1
Skewness1.2274499
Sum2635475
Variance1.363912
MonotonicityNot monotonic
2023-04-17T16:11:20.449694image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1 750226
38.3%
0 475667
24.3%
2 459004
23.4%
3 183846
 
9.4%
4 60314
 
3.1%
5 19869
 
1.0%
6 6862
 
0.4%
7 2674
 
0.1%
8 1005
 
0.1%
9 392
 
< 0.1%
Other values (9) 335
 
< 0.1%
ValueCountFrequency (%)
0 475667
24.3%
1 750226
38.3%
2 459004
23.4%
3 183846
 
9.4%
4 60314
 
3.1%
5 19869
 
1.0%
6 6862
 
0.4%
7 2674
 
0.1%
8 1005
 
0.1%
9 392
 
< 0.1%
ValueCountFrequency (%)
20 1
 
< 0.1%
17 1
 
< 0.1%
16 2
 
< 0.1%
15 2
 
< 0.1%
14 6
 
< 0.1%
13 22
 
< 0.1%
12 37
 
< 0.1%
11 91
 
< 0.1%
10 173
< 0.1%
9 392
< 0.1%

Defects7
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.020019447
Minimum0
Maximum7
Zeros1924033
Zeros (%)98.2%
Negative0
Negative (%)0.0%
Memory size29.9 MiB
2023-04-17T16:11:20.522015image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.15235217
Coefficient of variation (CV)7.6102084
Kurtosis97.467247
Mean0.020019447
Median Absolute Deviation (MAD)0
Skewness8.7806859
Sum39242
Variance0.023211182
MonotonicityNot monotonic
2023-04-17T16:11:20.579674image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 1924033
98.2%
1 33439
 
1.7%
2 2430
 
0.1%
3 234
 
< 0.1%
4 53
 
< 0.1%
6 2
 
< 0.1%
5 2
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
0 1924033
98.2%
1 33439
 
1.7%
2 2430
 
0.1%
3 234
 
< 0.1%
4 53
 
< 0.1%
5 2
 
< 0.1%
6 2
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
6 2
 
< 0.1%
5 2
 
< 0.1%
4 53
 
< 0.1%
3 234
 
< 0.1%
2 2430
 
0.1%
1 33439
 
1.7%
0 1924033
98.2%

Defects8
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.025823464
Minimum0
Maximum7
Zeros1921974
Zeros (%)98.1%
Negative0
Negative (%)0.0%
Memory size29.9 MiB
2023-04-17T16:11:20.645858image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.20248952
Coefficient of variation (CV)7.8412994
Kurtosis120.38538
Mean0.025823464
Median Absolute Deviation (MAD)0
Skewness9.8514491
Sum50619
Variance0.041002004
MonotonicityNot monotonic
2023-04-17T16:11:20.706701image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 1921974
98.1%
1 28385
 
1.4%
2 7750
 
0.4%
3 1683
 
0.1%
4 335
 
< 0.1%
5 58
 
< 0.1%
6 8
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
0 1921974
98.1%
1 28385
 
1.4%
2 7750
 
0.4%
3 1683
 
0.1%
4 335
 
< 0.1%
5 58
 
< 0.1%
6 8
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
6 8
 
< 0.1%
5 58
 
< 0.1%
4 335
 
< 0.1%
3 1683
 
0.1%
2 7750
 
0.4%
1 28385
 
1.4%
0 1921974
98.1%

Defects9
Real number (ℝ)

SKEWED  ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0020018427
Minimum0
Maximum6
Zeros1957425
Zeros (%)99.9%
Negative0
Negative (%)0.0%
Memory size29.9 MiB
2023-04-17T16:11:20.774736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.060031664
Coefficient of variation (CV)29.988203
Kurtosis1908.0641
Mean0.0020018427
Median Absolute Deviation (MAD)0
Skewness39.034794
Sum3924
Variance0.0036038006
MonotonicityNot monotonic
2023-04-17T16:11:20.830679image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 1957425
99.9%
1 1937
 
0.1%
2 584
 
< 0.1%
3 192
 
< 0.1%
4 39
 
< 0.1%
5 15
 
< 0.1%
6 2
 
< 0.1%
ValueCountFrequency (%)
0 1957425
99.9%
1 1937
 
0.1%
2 584
 
< 0.1%
3 192
 
< 0.1%
4 39
 
< 0.1%
5 15
 
< 0.1%
6 2
 
< 0.1%
ValueCountFrequency (%)
6 2
 
< 0.1%
5 15
 
< 0.1%
4 39
 
< 0.1%
3 192
 
< 0.1%
2 584
 
< 0.1%
1 1937
 
0.1%
0 1957425
99.9%

AgeDays
Real number (ℝ)

Distinct12223
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6480.6493
Minimum845
Maximum44876
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.9 MiB
2023-04-17T16:11:20.914649image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum845
5-th percentile3129
Q15267
median6486
Q37797
95-th percentile9483
Maximum44876
Range44031
Interquartile range (IQR)2530

Descriptive statistics

Standard deviation1976.1009
Coefficient of variation (CV)0.30492329
Kurtosis0.71748696
Mean6480.6493
Median Absolute Deviation (MAD)1266
Skewness0.15815978
Sum1.270333 × 1010
Variance3904974.8
MonotonicityNot monotonic
2023-04-17T16:11:21.005450image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8872 4614
 
0.2%
9237 4311
 
0.2%
9602 4223
 
0.2%
8507 3882
 
0.2%
9968 3072
 
0.2%
10333 2288
 
0.1%
8141 2146
 
0.1%
10698 1761
 
0.1%
11063 1569
 
0.1%
11429 1567
 
0.1%
Other values (12213) 1930761
98.5%
ValueCountFrequency (%)
845 1
 
< 0.1%
846 1
 
< 0.1%
847 4
< 0.1%
851 1
 
< 0.1%
852 2
 
< 0.1%
854 3
 
< 0.1%
858 1
 
< 0.1%
859 1
 
< 0.1%
860 1
 
< 0.1%
861 8
< 0.1%
ValueCountFrequency (%)
44876 1
< 0.1%
44863 1
< 0.1%
44680 1
< 0.1%
32978 1
< 0.1%
31729 1
< 0.1%
25308 1
< 0.1%
24943 1
< 0.1%
24841 1
< 0.1%
24138 1
< 0.1%
23117 1
< 0.1%

Interactions

2023-04-17T16:11:05.344208image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:23.893574image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:26.685579image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:29.277582image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:31.992539image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:34.688484image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:37.447622image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:40.210744image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:43.016506image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:45.705050image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:48.390629image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:51.241744image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:53.948007image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:56.864582image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:11:00.002038image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:11:02.696352image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:11:05.512903image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:24.068426image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:26.840101image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:29.444024image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:32.152963image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:34.866112image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:37.614269image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:40.384134image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:43.178186image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:45.873153image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:48.565384image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:51.411253image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:54.142998image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:57.025589image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:11:00.165417image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:11:02.862587image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:11:05.684140image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:24.245089image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:27.001187image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:29.608955image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:32.315733image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:35.040566image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:37.791726image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:40.559170image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:43.343565image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:46.041137image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:48.749632image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:51.587712image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:54.325707image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:57.191304image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:11:00.348081image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:11:03.031449image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:11:05.854635image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:24.417502image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:27.162336image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:29.776957image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:32.475254image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:35.209631image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:37.973210image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:40.737019image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:43.514835image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:46.214352image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:48.927195image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:51.755147image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:54.502847image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:57.360989image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:11:00.532906image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:11:03.195600image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:11:06.029294image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:24.590004image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:27.322995image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:29.944763image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:32.644422image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:35.369756image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:38.144197image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:40.909483image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:43.680136image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:46.382954image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:49.108015image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:51.918077image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:54.682513image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:57.528717image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:11:00.705896image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:11:03.355667image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:11:06.207234image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:24.765721image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:27.489239image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:30.119339image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:32.815605image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:35.547763image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:38.349627image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:41.085460image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:43.851506image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:46.559698image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:49.290536image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:52.086331image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:54.866977image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:57.698356image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:11:00.872691image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:11:03.523719image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:11:06.378791image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:24.938044image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:27.650103image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:30.294185image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:32.979563image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:35.717849image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:38.531280image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:41.253567image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:44.022847image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:46.732140image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:49.466975image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:52.254378image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:55.044668image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:57.861390image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:11:01.037394image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:11:03.683137image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:11:06.554494image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:25.107783image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:27.813024image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:30.463075image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:33.147365image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:35.882047image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:38.699203image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:41.428251image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:44.190051image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:46.899029image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:49.646903image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:52.427009image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:55.222535image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:58.037907image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:11:01.200647image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:11:03.846866image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:11:06.735130image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:25.278531image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:27.975678image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:30.632390image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:33.320468image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:36.061068image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:38.867831image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:41.607524image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:44.355209image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:47.064568image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:49.826392image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:52.592759image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:55.400116image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:58.205440image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:11:01.362938image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:11:04.017890image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:11:06.912881image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:25.452893image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:28.138249image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:30.803772image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:33.489329image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:36.248940image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:39.041600image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:41.783141image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:44.523168image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:47.230761image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:50.002106image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:52.759408image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:55.579653image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:58.380414image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:11:01.528370image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:11:04.188876image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:11:07.093117image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:25.643365image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:28.302636image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:30.979483image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:33.660148image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:36.419097image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:39.208643image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:41.960454image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:44.690923image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:47.398292image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:50.175153image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:52.931756image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:55.759486image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:58.556194image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:11:01.702045image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:11:04.353483image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:11:07.262136image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:25.814341image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:28.459887image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:31.148027image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:33.835329image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:36.582297image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:39.367240image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:42.128427image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:44.850966image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:47.558803image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:50.350729image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:53.102577image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:55.957395image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:58.720835image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:11:01.866796image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:11:04.510797image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:11:07.436661image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:25.992601image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:28.628719image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:31.322056image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:34.010405image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:36.771281image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:39.539454image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:42.310063image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:45.030035image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:47.731731image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:50.539505image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:53.271334image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:56.162593image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:59.345633image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:11:02.046639image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:11:04.677991image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:11:07.611341image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:26.165152image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:28.791027image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:31.492701image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:34.180577image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:36.944306image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:39.707740image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:42.487468image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:45.202132image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:47.894751image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:50.720563image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:53.445485image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:56.345142image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:59.509598image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:11:02.211790image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:11:04.849487image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:11:07.783565image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:26.355113image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:28.951905image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:31.664485image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:34.357623image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:37.109659image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:39.872898image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:42.671333image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:45.374757image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:48.061521image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:50.901230image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:53.612984image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:56.527377image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:59.671607image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:11:02.376265image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:11:05.012814image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:11:07.948763image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:26.529266image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:29.110862image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:31.835544image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:34.519520image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:37.274584image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:40.041487image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:42.854052image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:45.543275image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:48.224587image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:51.081190image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:53.781157image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:56.707321image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:10:59.838325image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:11:02.540946image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-17T16:11:05.171427image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Missing values

2023-04-17T16:11:09.039348image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-17T16:11:11.683100image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

TypeVINDateMotorTypeMakeVehicleTypeModelVehicleClassFirstRegistrationDateKmResultWeekdayDefectsADefectsBDefectsCDefects0Defects1Defects2Defects3Defects4Defects5Defects6Defects7Defects8Defects9AgeDays
StationId
3311pravidelnáW0LF7A0A6CV6206922020-01-02M9RA630OPELOSOBNÍ AUTOMOBILVIVAROM12012-02-29111040způsobilé430001100110004065
3311pravidelnáWDB1241251A6433422020-01-02602.912MERCEDESOSOBNÍ AUTOMOBIL124M11988-01-01503753způsobilé4300011000100012890
3311pravidelnáWVWZZZ7MZWV0584282020-01-02AFNVOLKSWAGENOSOBNÍ AUTOMOBILSHARANM11998-05-05266094způsobilé450002010120009113
3713pravidelnáVF625GPA0000005782020-01-02DXI11RENAULTNÁKLADNÍ AUTOMOBILPREMIUMN32006-11-28955721způsobilé450000101120005984
3634pravidelnáYS2R4X200053538172020-01-02DC13 125SCANIANÁKLADNÍ AUTOMOBILR 480N32014-05-02267641způsobilé440001100120003272
3716pravidelnáYS2R4X200054281392020-01-02DC13 115SCANIANÁKLADNÍ AUTOMOBILR410N32016-06-01430463způsobilé440001110010002511
3814pravidelnáWV1ZZZ7HZ4H0375452020-01-02AXBVOLKSWAGENNÁKLADNÍ AUTOMOBILTRANSPORTERN12003-10-27535859způsobilé440001003120007112
3219pravidelnáJMZGG14R2411940622020-01-02RFMAZDAOSOBNÍ AUTOMOBIL6M12003-12-11214688způsobilé450002000220007067
3412pravidelnáXLRAEL2700L4812942020-01-02PX-7 217 K1DAFNÁKLADNÍ AUTOMOBILL2EN3N32019-01-0278105způsobilé420000001110001566
3810pravidelnáL5YACBPA0811928812020-01-02BN139QMBZNENMOTOCYKLZN50QT19LA2010-10-134848částečně způsobilé403000002010004569
TypeVINDateMotorTypeMakeVehicleTypeModelVehicleClassFirstRegistrationDateKmResultWeekdayDefectsADefectsBDefectsCDefects0Defects1Defects2Defects3Defects4Defects5Defects6Defects7Defects8Defects9AgeDays
StationId
3234pravidelnáTMBZZZ1UXX21420822020-12-31AEEŠKODAOSOBNÍ AUTOMOBILOCTAVIAM11999-01-08189872způsobilé430001001010008865
3108pravidelnáWDB1680091J6386642020-12-31668.942MERCEDES-BENZOSOBNÍ AUTOMOBILA 170M12001-12-07220652částečně způsobilé462012012110007801
3521před registrací - opakovanáTMADB81SABJ0575362020-12-31D4FBHYUNDAIOSOBNÍ AUTOMOBILi30M12011-09-22152868způsobilé420001000100004225
3609opakovanáVF1KCR8BF317191792020-12-31K9KB7RENAULTNÁKLADNÍ AUTOMOBILKANGOON12004-10-04171473způsobilé450002000120006769
3108pravidelnáVF1BT1K06389830502020-12-31M9RG7RENAULTOSOBNÍ AUTOMOBILLAGUNAM12008-01-10202528způsobilé410000001000005576
3618pravidelnáTSMNZC72S002505352020-12-31K12BSUZUKIOSOBNÍ AUTOMOBILSWIFTM12012-07-2499648způsobilé410000001000003919
3521před registracíWVWZZZ7MZ8V0107822020-12-31BRTVWOSOBNÍ AUTOMOBILSHARANM12007-12-03198342způsobilé430000100110005614
3521pravidelnáYS2R4X200055365472020-12-31DC13148SCANIANÁKLADNÍ AUTOMOBILR450N32019-01-24235080způsobilé410000010000001544
3609pravidelnáWV2ZZZ2KZ9X0544922020-12-31BSXVWOSOBNÍ AUTOMOBILCADDY LIFEM12008-11-03229772způsobilé430000000210005278
3609pravidelnáWF0KXXBDFKYS384272020-12-31D2FAFORDNÁKLADNÍ AUTOMOBILTRANSITN12002-07-10360545nezpůsobilé468513104370007586

Duplicate rows

Most frequently occurring

TypeVINDateMotorTypeMakeVehicleTypeModelVehicleClassFirstRegistrationDateKmResultWeekdayDefectsADefectsBDefectsCDefects0Defects1Defects2Defects3Defects4Defects5Defects6Defects7Defects8Defects9AgeDays# duplicates
0evidenční6FPPXXMJ2PGE024312020-12-04SA2WFORDNÁKLADNÍ AUTOMOBILRANGERN1G2017-05-0583676částečně způsobilé5010100000000021732
1evidenčníJMB0NV460WJ0019792020-03-244M40MITSUBISHIOSOBNÍ AUTOMOBILPAJEROM1G1998-01-01377413částečně způsobilé2010100000000092372
2evidenčníJN1TENT30U00105132020-03-09YD22NISSANOSOBNÍ AUTOMOBILXTRAILM1G2002-04-09275656částečně způsobilé1010100000000076782
3evidenčníKL1CG2669CB0892022020-07-24Z22D1CHEVROLETOSOBNÍ AUTOMOBILCAPTIVAM12013-03-04147801částečně způsobilé5010100000000036962
4evidenčníMMBJRK7403D0124132020-08-314D56MITSUBISHINÁKLADNÍ AUTOMOBILL 200N1G2002-11-22225925částečně způsobilé1010100000000074512
5evidenčníTMBGA61Z5920496892020-09-25BSEŠKODAOSOBNÍ AUTOMOBILOCTAVIAM12009-04-09143312částečně způsobilé5010100000000051212
6evidenčníTMBJF75L4F60105522020-07-27CBZŠKODAOSOBNÍ AUTOMOBILYETIM12014-09-0114895částečně způsobilé1010100000000031502
7evidenčníTMBJJ7NE9G01332972020-06-12CRMŠKODAOSOBNÍ AUTOMOBILOCTAVIAM12016-01-06108452částečně způsobilé5010100000000026582
8evidenčníTMBLK7NS4J80413252020-12-02DFHŠKODAOSOBNÍ AUTOMOBILKODIAQM12017-11-0386462částečně způsobilé3010100000000019912
9evidenčníU5YPH814AKL7103882020-08-12G4FDKIAOSOBNÍ AUTOMOBILSPORTAGEM12020-04-083482částečně způsobilé3010100000000011042